295 lines
8.4 KiB
Python
295 lines
8.4 KiB
Python
# Copyright 2017 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from mycroft.tts import TTS, TTSValidator
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from mycroft.tts.remote_tts import RemoteTTS
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from mycroft.util.log import LOG
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from mycroft.util.format import pronounce_number
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from mycroft.util import play_wav, get_cache_directory, create_signal
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from requests_futures.sessions import FuturesSession
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from urllib import parse
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from .mimic_tts import VISIMES
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import math
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import base64
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import os
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import hashlib
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import re
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def break_chunks(l, n):
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"""Yield successive n-sized chunks from l."""
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for i in range(0, len(l), n):
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yield " ".join(l[i:i + n])
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def split_by_chunk_size(text, chunk_size):
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"""split text into word chunks by chunk_size size
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Args:
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text (str): text to split
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chunk_size (int): chunk size
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Returns:
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list: list of text chunks
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"""
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text_list = text.split()
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if len(text_list) <= chunk_size:
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return [text]
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if chunk_size < len(text_list) < (chunk_size * 2):
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return list(break_chunks(
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text_list,
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int(math.ceil(len(text_list) / 2))
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))
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elif (chunk_size * 2) < len(text_list) < (chunk_size * 3):
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return list(break_chunks(
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text_list,
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int(math.ceil(len(text_list) / 3))
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))
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elif (chunk_size * 3) < len(text_list) < (chunk_size * 4):
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return list(break_chunks(
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text_list,
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int(math.ceil(len(text_list) / 4))
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))
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else:
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return list(break_chunks(
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text_list,
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int(math.ceil(len(text_list) / 5))
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))
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def split_by_punctuation(text, chunk_size):
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"""split text by punctuations
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i.e "hello, world" -> ["hello", "world"]
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Args:
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text (str): text to split
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chunk_size (int): size of each chunk
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Returns:
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list: list of sentence chunk
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"""
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punctuations = [',', '.', '-', '?', '!', ':', ';']
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text_list = text.split()
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splits = None
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if len(text_list) >= chunk_size:
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for punc in punctuations:
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if punc in text:
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splits = text.split(punc)
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break
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# TODO: check if splits are to small, combined them
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return splits
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def add_punctuation(text):
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"""add punctuation at the end of each chunk. Mimic2
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expects a form of punctuation
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"""
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punctuations = ['.', '?', '!']
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if len(text) < 1:
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return text
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if len(text) < 10:
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if text[-1] in punctuations:
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if text[-1] != ".":
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return text[:-1] + "."
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if text[-1] not in punctuations:
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text += '.'
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return text
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def sentence_chunker(text, chunk_size, split_by_punc=True):
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"""split sentences into chunks. if split_by_punc is True,
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sentences will be split into chunks by punctuations first
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then those chunks will be split by chunk size
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Args:
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text (str): text to split
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chunk_size (int): size of each chunk
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split_by_punc (bool, optional): Defaults to True.
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Returns:
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list: list of text chunks
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"""
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text_list = text.split()
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# if initial text is 1.3 times chunk size, no need to split
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# if the chracter count is less then 55
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if len(text_list) <= chunk_size * 1.3:
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if len(text) < 55:
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return [add_punctuation(text)]
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# split text by punctuations if split_by_punc set to true
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punc_splits = None
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if split_by_punc:
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punc_splits = split_by_punctuation(text, chunk_size)
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# split text by chunk size
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chunks = []
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if punc_splits:
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for sentence in punc_splits:
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sentence = sentence.strip()
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chunks += split_by_chunk_size(sentence, chunk_size)
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# split text by chunk size
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else:
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chunks += split_by_chunk_size(text, chunk_size)
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chunks = [add_punctuation(chunk) for chunk in chunks]
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return chunks
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class Mimic2(TTS):
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def __init__(self, lang, config):
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super(Mimic2, self).__init__(
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lang, config, Mimic2Validator(self)
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)
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self.url = config['url']
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self.session = FuturesSession()
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chunk_size = config.get('chunk_size')
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self.chunk_size = \
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chunk_size if chunk_size is not None else 10
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def _save(self, data):
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"""saves .wav files in tmp
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Args:
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data (byes): wav data
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"""
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with open(self.filename, 'wb') as f:
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f.write(data)
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def _play(self, req):
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"""play wav file after saving to tmp
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Args:
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req (object): requests object
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"""
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if req.status_code == 200:
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self._save(req.content)
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play_wav(self.filename).communicate()
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else:
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LOG.error(
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'%s Http Error: %s for url: %s' %
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(req.status_code, req.reason, req.url))
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def build_request_params(self, sentence):
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"""RemoteTTS expects this method as abc.abstractmethod"""
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pass
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def _requests(self, chunks):
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"""create asynchronous request list
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Args:
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chunks (list): list of text to synthesize
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Returns:
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list: list of FutureSession objects
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"""
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reqs = []
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for chunk in chunks:
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if len(chunk) > 0:
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url = self.url + parse.quote(chunk)
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req_route = url + "&visimes=True"
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reqs.append(self.session.get(req_route))
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return reqs
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def visime(self, phonemes):
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"""maps phonemes to visemes encoding
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Args:
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phonemes (list): list of tuples (phoneme, time_start)
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Returns:
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list: list of tuples (viseme_encoding, time_start)
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"""
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visemes = []
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for pair in phonemes:
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if pair[0]:
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phone = pair[0].lower()
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else:
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# if phoneme doesn't exist use
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# this as placeholder since it
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# is the most common one "3"
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phone = 'z'
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vis = VISIMES.get(phone)
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vis_dur = float(pair[1])
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visemes.append((vis, vis_dur))
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return visemes
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def _normalized_numbers(self, sentence):
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"""normalized numbers to word equivalent.
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Args:
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sentence (str): setence to speak
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Returns:
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stf: normalized sentences to speak
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"""
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try:
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numbers = re.findall(r'\d+', sentence)
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normalized_num = [
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(num, pronounce_number(int(num)))
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for num in numbers
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]
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for num, norm_num in normalized_num:
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sentence = sentence.replace(num, norm_num, 1)
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except TypeError:
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LOG.exception("type error in mimic2_tts.py _normalized_numbers()")
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return sentence
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def execute(self, sentence, ident=None):
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"""request and play mimic2 wav audio
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Args:
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sentence (str): sentence to synthesize from mimic2
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ident (optional): Defaults to None.
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"""
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create_signal("isSpeaking")
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sentence = self._normalized_numbers(sentence)
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chunks = sentence_chunker(sentence, self.chunk_size)
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for idx, req in enumerate(self._requests(chunks)):
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results = req.result().json()
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audio = base64.b64decode(results['audio_base64'])
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vis = self.visime(results['visimes'])
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key = str(hashlib.md5(
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chunks[idx].encode('utf-8', 'ignore')).hexdigest())
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wav_file = os.path.join(
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get_cache_directory("tts"),
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key + '.' + self.audio_ext
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)
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with open(wav_file, 'wb') as f:
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f.write(audio)
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self.queue.put((self.audio_ext, wav_file, vis, ident))
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class Mimic2Validator(TTSValidator):
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def __init__(self, tts):
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super(Mimic2Validator, self).__init__(tts)
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def validate_lang(self):
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# TODO
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pass
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def validate_connection(self):
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# TODO
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pass
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def get_tts_class(self):
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return Mimic2
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